Integrating native AI support into the network architecture is an essential
objective of 6G. Federated Learning (FL) emerges as a potential paradigm,
facilitating decentralized AI model training across a diverse range of devices
under the coordination of a central server. However, several challenges hinder
its wide application in the 6G context, such as malicious attacks and privacy
snooping on local model updates, and centralization pitfalls. This work
proposes a trusted architecture for supporting FL, which utilizes Distributed
Ledger Technology (DLT) and Graph Neural Network (GNN), including three key
features. First, a pre-processing layer employing homomorphic encryption is
incorporated to securely aggregate local models, preserving the privacy of
individual models. Second, given the distributed nature and graph structure
between clients and nodes in the pre-processing layer, GNN is leveraged to
identify abnormal local models, enhancing system security. Third, DLT is
utilized to decentralize the system by selecting one of the candidates to
perform the central server's functions. Additionally, DLT ensures reliable data
management by recording data exchanges in an immutable and transparent ledger.
The feasibility of the novel architecture is validated through simulations,
demonstrating improved performance in anomalous model detection and global
model accuracy compared to relevant baselines.

利用分布式账本技术和图神经网络构建的受信任的体系结构，解决了 6G 环境下联邦学习的挑战，包括隐私保护和安全问题，并验证了其在异常模型检测和全局模型准确性方面相对于相关基准的性能提升。

推进 6G 中的联邦学习：基于图形分析的可信架构

Advancing Federated Learning in 6G: A Trusted Architecture with  Graph-based Analysis

Distributed ledger systems have become more prominent and successful in
recent years, with a focus on blockchains and cryptocurrency. This has led to
various misunderstandings about both the technology itself and its
capabilities, as in many cases blockchain and cryptocurrency is used
synonymously and other applications are often overlooked. Therefore, as a
whole, the view of distributed ledger technology beyond blockchains and
cryptocurrencies is very limited. Existing vocabularies and ontologies often
focus on single aspects of the technology, or in some cases even just on one
product. This potentially leads to other types of distributed ledgers and their
possible use cases being neglected. In this paper, we present a knowledge graph
and an ontology for distributed ledger technologies, which includes security
considerations to model aspects such as threats and vulnerabilities,
application domains, as well as relevant standards and regulations. Such a
knowledge graph improves the overall understanding of distributed ledgers,
reveals their strengths, and supports the work of security personnel, i.e.
analysts and system architects. We discuss potential uses and follow semantic
web best practices to evaluate and publish the ontology and knowledge graph.

本文提出了分布式账本技术的知识图谱和本体论，包括威胁和漏洞，应用领域以及相关标准和法规，从而改善人们对分布式账本技术的整体认识，揭示其优势，并支持安全人员的工作。

建立分布式账本技术知识图谱

Building a Knowledge Graph of Distributed Ledger Technologies

Federated learning (FL) is a promising way to allow multiple data owners
(clients) to collaboratively train machine learning models without compromising
data privacy. Yet, existing FL solutions usually rely on a centralized
aggregator for model weight aggregation, while assuming clients are honest.
Even if data privacy can still be preserved, the problem of single-point
failure and data poisoning attack from malicious clients remains unresolved. To
tackle this challenge, we propose to use distributed ledger technology (DLT) to
achieve FLock, a secure and reliable decentralized Federated Learning system
built on blockchain. To guarantee model quality, we design a novel peer-to-peer
(P2P) review and reward/slash mechanism to detect and deter malicious clients,
powered by on-chain smart contracts. The reward/slash mechanism, in addition,
serves as incentives for participants to honestly upload and review model
parameters in the FLock system. FLock thus improves the performance and the
robustness of FL systems in a fully P2P manner.

使用去中心化的基于区块链的安全可靠的 FLock 系统，采用分布式账本技术来保护数据隐私并设计了一种新增奖励和惩罚机制以检测和防止恶意客户，并提供激励以确保 FLock 系统中的模型参数上传的诚实性和模型质量

使用区块链防御联邦学习中的恶意行为

FLock: Defending Malicious Behaviors in Federated Learning with Blockchain

We present ARCHANGEL; a de-centralised platform for ensuring the long-term
integrity of digital documents stored within public archives. Document
integrity is fundamental to public trust in archives. Yet currently that trust
is built upon institutional reputation --- trust at face value in a centralised
authority, like a national government archive or University. ARCHANGEL proposes
a shift to a technological underscoring of that trust, using distributed ledger
technology (DLT) to cryptographically guarantee the provenance, immutability
and so the integrity of archived documents. We describe the ARCHANGEL
architecture, and report on a prototype of that architecture build over the
Ethereum infrastructure. We report early evaluation and feedback of ARCHANGEL
from stakeholders in the research data archives space.

ARCHANGEL 是一个去中心化平台，使用分布式账本技术来加密保证公共存档中数字文献的始发性、不可变性和完整性。我们描述了 ARCHANGEL 的体系结构，并报告了基于 Ethereum 基础架构的原型。我们还从研究数据档案空间的相关人员获得了早期评估和反馈。